{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from numpy import math\n",
    "\n",
    "loansData = pd.read_csv('https://spark-public.s3.amazonaws.com/dataanalysis/loansData.csv')\n",
    "\n",
    "#Note, a schema can be found here : https://github.com/herrfz/dataanalysis/blob/master/assignment1/Assignment1.pdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Amount.Requested', 'Amount.Funded.By.Investors', 'Interest.Rate',\n",
       "       'Loan.Length', 'Loan.Purpose', 'Debt.To.Income.Ratio', 'State',\n",
       "       'Home.Ownership', 'Monthly.Income', 'FICO.Range',\n",
       "       'Open.CREDIT.Lines', 'Revolving.CREDIT.Balance',\n",
       "       'Inquiries.in.the.Last.6.Months', 'Employment.Length'], dtype=object)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loansData.columns.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "81174     6541.67\n",
       "99592     4583.33\n",
       "80059    11500.00\n",
       "15825     3833.33\n",
       "33182     3195.00\n",
       "Name: Monthly.Income, dtype: float64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loansData['Monthly.Income'][0:5] # first five rows of Interest.Rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ifRZEROQSWCx0pPq13sxnXCpnA9TPpwcWCyIicoo9C/YsiMjg2LMgIiKXwGKhI9XnTZnP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\nAS6yRgLVeDXAAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1062fc4d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "inc = loansData['Monthly.Income']\n",
    "h = inc.hist()\n",
    "plt.title('Histogram of Monthly Income')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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nziE+Ph4dO3bEsGHDUFJSYr1PSkoKoqKiEB0dja1btzZO9W5G3zmjResCFGbR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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x106318850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "loansData['Monthly.LogIncome'] = [ math.log(x) for x in inc ]\n",
    "plt.figure()\n",
    "h = loansData['Monthly.LogIncome'].hist()\n",
    "plt.title('Histogram of Log(Monthly Income)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    2499.000000\n",
       "mean        8.501915\n",
       "std         0.523019\n",
       "min         6.377577\n",
       "25%         8.160518\n",
       "50%         8.517193\n",
       "75%         8.824678\n",
       "max        11.540054\n",
       "dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loansData['Monthly.LogIncome'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
